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A GPU-Based Method in Recovering the Full 3D Deformation Field Using Multiple 2D Fluoroscopic Views in Lung Navigation

机译:基于GPU的方法在肺导航中使用多个2D透视探测恢复完整的3D变形场

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In this work, we present a GPU implementation for 2D-3D deformable registration to fully recover the 3D lung-deformation-field using multiple 2D fluoroscopic views. Intensive computational requirement for 2D-3D deformable registration prevents its clinical usage. To make our method clinically applicable, we developed a GPU kernel system to reduce the computation time from hours to seconds. The proposed kernel system employed a total of six GPU kernels. These six kernels serve the basis of two kernel pipelines: cost calculation and gradient calculation, the outputs of which are fed into a CPU-based optimizer to find the optimal deformation field. The evaluation was performed on both synthetic and real cases. The accuracy, evaluated using feature points, was 2.03mm on an average. A speedup factor of 690 could be reached over pure CPU-based implementation.
机译:在这项工作中,我们为2D-3D可变形注册提供了GPU实现,以使用多个2D透视视图完全恢复3D肺变形场。 2D-3D可变形登记的密集计算要求可防止其临床使用情况。为了使我们的方法适用,我们开发了一个GPU内核系统,以将计算时间从小时缩短到秒。所提出的内核系统共有六个GPU内核。这六个内核提供了两个内核管道的基础:成本计算和梯度计算,其输出被馈送到基于CPU的优化器中以找到最佳变形字段。评价是对合成和实际情况进行的。使用特征点评估的准确性平均为2.03mm。可以通过纯CPU的实现来达到690的加速因子。

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